Sub-modularity and Antenna Selection in MIMO systems

In this paper, we show that the optimal receive antenna subset selection problem for maximizing the mutual information in a point-to-point MIMO system is sub-modular. Consequently, a greedy step-wise optimization approach, where at each step an antenna that maximizes the incremental gain is added to the existing antenna subset, is guaranteed to be within a (1 - 1/e) fraction of the global optimal value. For a single antenna equipped source and destination with multiple relays, we show that the relay antenna selection problem to maximize the mutual information is modular, when complete channel state information is available at the relays. As a result a greedy step-wise optimization approach leads to an optimal solution for the relay antenna selection problem with linear complexity in comparison to the brute force search that incurs exponential complexity

Distributed Antenna Selection for Massive MIMO using Reversing Petri Nets

Distributed antenna selection for Distributed Massive MIMO (Multiple Input Multiple Output) communication systems reduces computational complexity compared to centralised approaches, and provides high fault tolerance while retaining diversity and spatial multiplexity. We propose a novel distributed algorithm for antenna selection and show its advantage over existing centralised and distributed solutions. The proposed algorithm is shown to perform well with imperfect channel state information, and to execute a small number of simple computational operations per node, converging fast to a steady state. We base it on Reversing Petri Nets, a variant of Petri nets inspired by reversible computation, capable of both forward and backward execution while obeying conservation laws.Comment: Copyright 2019 IEEE, Wireless Communications Letter

Asymptotic Upper Capacity Bound for Receive Antenna Selection in Massive MIMO Systems

This paper studies the receive antenna selection in massive multiple-input multiple-output (MIMO) system. The receiver, equipped with a large-scale antenna array whose size is much larger than that of the transmitter, selects a subset of antennas to receive messages. A low-complexity asymptotic approximated upper capacity bound is derived in the limit of massive MIMO systems over independent and identical distributed flat fading Rayleigh channel, assuming that the channel side information (CSI) is only available at the receiver. Furthermore, the asymptotic theory is separately applied to two scenarios which is based on whether the total amount of the selected antennas exceed that of the transmit antennas. Besides analytical derivations, simulation results are provided to demonstrate the approximation precision of the asymptotic results and the tightness of the capacity bound.Comment: Submitted to ICC 201

Distributing Complexity: A New Approach to Antenna Selection for Distributed Massive MIMO

Antenna selection in Massive MIMO (Multiple Input Multiple Output) communication systems enables reduction of complexity, cost and power while keeping the channel capacity high and retaining the diversity, interference reduction, spatial multiplexity and array gains of Massive MIMO. We investigate the possibility of decentralised antenna selection both to parallelise the optimisation process and put the environment awareness to use. Results of experiments with two different power control rules and varying number of users show that a simple and computationally inexpensive algorithm can be used in real time. The algorithm we propose draws its foundations from self-organisation, environment awareness and randomness.Comment: 4 figure

Reversible Computation in Wireless Communications

This chapter presents the pioneering work in applying reversible computation paradigms to wireless communications. These applications range from developing reversible hardware architectures for underwater acoustic communications to novel distributed optimisation procedures in large radio-frequency antenna arrays based on reversing Petri nets. Throughout the chapter, we discuss the rationale for introducing reversible computation in the domain of wireless communications, exploring the inherently reversible properties of communication channels and systems formed by devices in a wireless network.Comment: Book chapter in IC 1405 COST Action on Reversible Computation book. arXiv admin note: text overlap with arXiv:1911.0643

Optimizing Beams and Bits: A Novel Approach for Massive MIMO Base-Station Design

We consider the problem of jointly optimizing ADC bit resolution and analog beamforming over a frequency-selective massive MIMO uplink. We build upon a popular model to incorporate the impact of low bit resolution ADCs, that hitherto has mostly been employed over flat-fading systems. We adopt weighted sum rate (WSR) as our objective and show that WSR maximization under finite buffer limits and important practical constraints on choices of beams and ADC bit resolutions can equivalently be posed as constrained submodular set function maximization. This enables us to design a constant-factor approximation algorithm. Upon incorporating further enhancements we obtain an efficient algorithm that significantly outperforms state-of-the-art ones.Comment: Tech. Report. Appeared in part in IEEE ICNC 2019. Added few more comments and corrected minor typo

Massive MIMO Antenna Selection: Asymptotic Upper Capacity Bound and Partial CSI

Antenna selection (AS) is regarded as the key promising technology to reduce hardware cost but keep relatively high spectral efficiency in multi-antenna systems. By selecting a subset of antennas to transceive messages, AS greatly alleviates the requirement on Radio Frequency (RF) chains. This paper studies receive antenna selection in massive multiple-input multiple-output (MIMO) systems. The receiver, equipped with a large-scale antenna array whose size is much larger than that of the transmitter, selects a subset of antennas to receive messages. A low-complexity asymptotic approximated upper capacity bound is derived in the limit of massive MIMO systems over independent and identical distributed (i.i.d.) Rayleigh flat fading channel, assuming that the channel side information (CSI) is only available at the receiver. Furthermore, numerical simulations are provided to demonstrate the approximation precision of the asymptotic results and the tightness of the capacity bound. Besides the asymptotic analysis of the upper bound, more discussions on the ergodic capacity of the antenna selection systems are exhibited. By defining the number of corresponding rows in the channel matrix as the amount of acquired CSI, the relationship between the achievable channel capacity and the amount of acquired CSI is investigated. Our findings indicate that this relationship approximately follows the Pareto principle, i.e., most of the capacity can be achieved by acquiring a small portion of full CSI. Finally, on the basis of this observed law, an adaptive AS algorithm is proposed, which can achieve most of the transmission rate but requires much less CSI and computation complexity compared to state-of-the-art methods.Comment: Part of this article is submitted to 2019 IC